Automatic fake news detection is a challenging problem in deception detection. While evaluating the performance of deep learning-based models, if all the models are giving higher accuracy on a test dataset, it will make it harder to validate the performance of the deep learning models under consideration. So, we will need a complex problem to validate the performance of a deep learning model. LIAR is one such complex, much resent, labeled benchmark dataset which is publicly available for doing research on fake news detection to model statistical and machine learning approaches to combating fake news. In this work, a novel fake news detection system is implemented using Deep Neural Network models such as CNN, LSTM, BiLSTM, and the performance of their attention mechanism is evaluated by analyzing their performance in terms of Accuracy, Precision, Recall, and F1-score with training, validation and test datasets of LIAR.
{"title":"Attention-Based Deep Learning Models for Detection of Fake News in Social Networks","authors":"S. Ramya, R. Eswari","doi":"10.4018/ijcini.295809","DOIUrl":"https://doi.org/10.4018/ijcini.295809","url":null,"abstract":"Automatic fake news detection is a challenging problem in deception detection. While evaluating the performance of deep learning-based models, if all the models are giving higher accuracy on a test dataset, it will make it harder to validate the performance of the deep learning models under consideration. So, we will need a complex problem to validate the performance of a deep learning model. LIAR is one such complex, much resent, labeled benchmark dataset which is publicly available for doing research on fake news detection to model statistical and machine learning approaches to combating fake news. In this work, a novel fake news detection system is implemented using Deep Neural Network models such as CNN, LSTM, BiLSTM, and the performance of their attention mechanism is evaluated by analyzing their performance in terms of Accuracy, Precision, Recall, and F1-score with training, validation and test datasets of LIAR.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84170355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.4018/IJCINI.20211001.OA40
Safia Bekhouche, Y. M. B. Ali
GPCRs are the largest family of cell surface receptors; many of them remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein the corresponding functional class. This classification step requires a good protein representation method and a robust classification algorithm. However, the complexity of this task could be increased because of the great number of GPCRs features in most databases, which produce combinatorial explosion. In order to reduce complexity and optimize classification, the authors propose to use bio-inspired metaheuristics for both the feature selection and the choice of the best couple (feature extraction strategy [FES], data mining algorithm [DMA]). The authors propose to use the BAT algorithm for extracting the pertinent features and the genetic algorithm to choose the best couple. They compared the results they obtained with two existing algorithms. Experimental results indicate the efficiency of the proposed system.
{"title":"Bio-Inspired Data Mining for Optimizing GPCR Function Identification","authors":"Safia Bekhouche, Y. M. B. Ali","doi":"10.4018/IJCINI.20211001.OA40","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA40","url":null,"abstract":"GPCRs are the largest family of cell surface receptors; many of them remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein the corresponding functional class. This classification step requires a good protein representation method and a robust classification algorithm. However, the complexity of this task could be increased because of the great number of GPCRs features in most databases, which produce combinatorial explosion. In order to reduce complexity and optimize classification, the authors propose to use bio-inspired metaheuristics for both the feature selection and the choice of the best couple (feature extraction strategy [FES], data mining algorithm [DMA]). The authors propose to use the BAT algorithm for extracting the pertinent features and the genetic algorithm to choose the best couple. They compared the results they obtained with two existing algorithms. Experimental results indicate the efficiency of the proposed system.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80953921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.4018/ijcini.20211001.oa17
Anbang Wang, Lihong Guo, Yuan Chen, Junjie Wang, Luo Liu, Yuanzhang Song
The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization. It has assumed significance in operations research and theoretical computer science. The problem was first formulated in 1930 and since then, has been one of the most extensively studied problems in optimization. In fact, it is used as a benchmark for many optimization methods. This paper represents a new method to addressing TSP using an improved version of cuckoo search (CS) with Stud (SCS) crossover operator. In SCS method, similar to genetic operators used in various metaheuristic algorithms, a Stud crossover operator that is originated from classical Stud genetic algorithm, is introduced into the CS with the aim of improving its effectiveness and reliability while dealing with TSP. Various test functions had been used to test this approach, and used subsequently to find the shortest path for Chinese TSP (CTSP). Experimental results presented clearly demonstrates SCS as a viable and attractive addition to the portfolio of swarm intelligence techniques.
{"title":"An Improved Cuckoo Search Algorithm With Stud Crossover for Chinese TSP Problem","authors":"Anbang Wang, Lihong Guo, Yuan Chen, Junjie Wang, Luo Liu, Yuanzhang Song","doi":"10.4018/ijcini.20211001.oa17","DOIUrl":"https://doi.org/10.4018/ijcini.20211001.oa17","url":null,"abstract":"The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization. It has assumed significance in operations research and theoretical computer science. The problem was first formulated in 1930 and since then, has been one of the most extensively studied problems in optimization. In fact, it is used as a benchmark for many optimization methods. This paper represents a new method to addressing TSP using an improved version of cuckoo search (CS) with Stud (SCS) crossover operator. In SCS method, similar to genetic operators used in various metaheuristic algorithms, a Stud crossover operator that is originated from classical Stud genetic algorithm, is introduced into the CS with the aim of improving its effectiveness and reliability while dealing with TSP. Various test functions had been used to test this approach, and used subsequently to find the shortest path for Chinese TSP (CTSP). Experimental results presented clearly demonstrates SCS as a viable and attractive addition to the portfolio of swarm intelligence techniques.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89273195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Previous research has shown that tracking algorithms cannot capture long-distance information and lead to the loss of the object when the object was deformed, the illumination changed, and the background was disturbed by similar objects. To remedy this, this article proposes an object-tracking method by introducing the Global Context attention module into the Multi-Domain Network (MDNet) tracker. This method can learn the robust feature representation of the object through the Global Context attention module to better distinguish the background from the object in the presence of interference factors. Extensive experiments on OTB2013, OTB2015, and UAV20L datasets show that the proposed method is significantly improved compared with MDNet and has competitive performance compared with more mainstream tracking algorithms. At the same time, the method proposed in this article achieves better results when the video sequence contains object deformation, illumination change, and background interference with similar objects.
{"title":"Object Tracking Based on Global Context Attention","authors":"Yucheng Wang, Xi Chen, Zhongjie Mao, Jia Yan","doi":"10.4018/ijcini.287595","DOIUrl":"https://doi.org/10.4018/ijcini.287595","url":null,"abstract":"Previous research has shown that tracking algorithms cannot capture long-distance information and lead to the loss of the object when the object was deformed, the illumination changed, and the background was disturbed by similar objects. To remedy this, this article proposes an object-tracking method by introducing the Global Context attention module into the Multi-Domain Network (MDNet) tracker. This method can learn the robust feature representation of the object through the Global Context attention module to better distinguish the background from the object in the presence of interference factors. Extensive experiments on OTB2013, OTB2015, and UAV20L datasets show that the proposed method is significantly improved compared with MDNet and has competitive performance compared with more mainstream tracking algorithms. At the same time, the method proposed in this article achieves better results when the video sequence contains object deformation, illumination change, and background interference with similar objects.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81991681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.4018/IJCINI.20211001.OA10
Hui Wang, Tie Cai, Wei-fang Cao
{"title":"Citrus Huanglongbing Recognition Algorithm Based on CKMOPSO","authors":"Hui Wang, Tie Cai, Wei-fang Cao","doi":"10.4018/IJCINI.20211001.OA10","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA10","url":null,"abstract":"","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91029984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.4018/ijcini.20211001.oa8
Fei Zhao, Qinghui Xu, Sanyou Zeng
Antenna design often requires dealing with multiple constraints in the requirements, and the designs can be modeled as constrained optimization problems (COPs). However, the constraints are usually very strange, and then the feasible solutions are hard to be found. At the same time, the robustness for antenna design is an important consideration as well. To solve the above issues, the combination of differential evolution algorithm (DE) and 3D-printing technique is presented to design a new crooked-wire antenna. In the design process, DE is adopted to handle the constraints since DE is simple and efficient in finding feasible solutions. The objective of the modeled COP, which is the sum of variance of the gain, axial ratio, and VSWR over the frequency band, is used to enhance the robustness of the antenna and widen the frequency band without additional computational cost. The precision of fabricating the antenna is ensured by using 3D-printing. The design of the NASA LADEE satellite antenna is chosen as an example to verify the method of this paper.
{"title":"Design of a Crooked-Wire Antenna by Differential Evolution and 3D Printing","authors":"Fei Zhao, Qinghui Xu, Sanyou Zeng","doi":"10.4018/ijcini.20211001.oa8","DOIUrl":"https://doi.org/10.4018/ijcini.20211001.oa8","url":null,"abstract":"Antenna design often requires dealing with multiple constraints in the requirements, and the designs can be modeled as constrained optimization problems (COPs). However, the constraints are usually very strange, and then the feasible solutions are hard to be found. At the same time, the robustness for antenna design is an important consideration as well. To solve the above issues, the combination of differential evolution algorithm (DE) and 3D-printing technique is presented to design a new crooked-wire antenna. In the design process, DE is adopted to handle the constraints since DE is simple and efficient in finding feasible solutions. The objective of the modeled COP, which is the sum of variance of the gain, axial ratio, and VSWR over the frequency band, is used to enhance the robustness of the antenna and widen the frequency band without additional computational cost. The precision of fabricating the antenna is ensured by using 3D-printing. The design of the NASA LADEE satellite antenna is chosen as an example to verify the method of this paper.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84946308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.4018/ijcini.20211001.oa9
Muhammad Salman Khan, Rene Richard, Heather Molyneaux, Danick Cote-Martel, Henry Jackson Kamalanathan Elango, Steve Livingstone, Manon Gaudet, David V. Trask
Security and Information Event Management (SIEM) systems require significant manual input; SIEM tools with machine learning minimizes this effort but are reactive and only effective if known attack patterns are captured by the configured rules and queries. Cyber threat hunting, a proactive method of detecting cyber threats without necessarily knowing the rules or pre-defined knowledge of threats, still requires significant manual effort and is largely missing the required machine intelligence to deploy autonomous analysis. This paper proposes a novel and interactive cognitive and predictive threat-hunting prototype tool to minimize manual configuration tasks by using machine intelligence and autonomous analytical capabilities. This tool adds proactive threat-hunting capabilities by extracting unique network communication behaviors from multiple endpoints autonomously while also providing an interactive UI with minimal configuration requirements and various cognitive visualization techniques to help cyber experts quickly spot events of cyber significance from high-dimensional data.
{"title":"Cyber Threat Hunting: A Cognitive Endpoint Behavior Analytic System","authors":"Muhammad Salman Khan, Rene Richard, Heather Molyneaux, Danick Cote-Martel, Henry Jackson Kamalanathan Elango, Steve Livingstone, Manon Gaudet, David V. Trask","doi":"10.4018/ijcini.20211001.oa9","DOIUrl":"https://doi.org/10.4018/ijcini.20211001.oa9","url":null,"abstract":"Security and Information Event Management (SIEM) systems require significant manual input; SIEM tools with machine learning minimizes this effort but are reactive and only effective if known attack patterns are captured by the configured rules and queries. Cyber threat hunting, a proactive method of detecting cyber threats without necessarily knowing the rules or pre-defined knowledge of threats, still requires significant manual effort and is largely missing the required machine intelligence to deploy autonomous analysis. This paper proposes a novel and interactive cognitive and predictive threat-hunting prototype tool to minimize manual configuration tasks by using machine intelligence and autonomous analytical capabilities. This tool adds proactive threat-hunting capabilities by extracting unique network communication behaviors from multiple endpoints autonomously while also providing an interactive UI with minimal configuration requirements and various cognitive visualization techniques to help cyber experts quickly spot events of cyber significance from high-dimensional data.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87314829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.4018/IJCINI.20211001.OA23
Ting Wang, Wing W. Y. Ng, Wendi Li, S. Kwong
Activation functions such as tanh and sigmoid functions are widely used in deep neural networks (DNNs) and pattern classification problems. To take advantage of different activation functions, this work proposes the broad autoencoder features (BAF). The BAF consists of four parallel-connected stacked autoencoders (SAEs), and each of them uses a different activation function, including sigmoid, tanh, relu, and softplus. The final learned features can merge by various nonlinear mappings from original input features with such a broad setting. It not only helps to excavate more information from the original input features through utilizing different activation functions, but also provides information diversity and increases the number of input nodes for classifier by parallel-connected strategy. Experimental results show that the BAF yields better-learned features and classification performances.
{"title":"Broad Autoencoder Features Learning for Classification Problem","authors":"Ting Wang, Wing W. Y. Ng, Wendi Li, S. Kwong","doi":"10.4018/IJCINI.20211001.OA23","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA23","url":null,"abstract":"Activation functions such as tanh and sigmoid functions are widely used in deep neural networks (DNNs) and pattern classification problems. To take advantage of different activation functions, this work proposes the broad autoencoder features (BAF). The BAF consists of four parallel-connected stacked autoencoders (SAEs), and each of them uses a different activation function, including sigmoid, tanh, relu, and softplus. The final learned features can merge by various nonlinear mappings from original input features with such a broad setting. It not only helps to excavate more information from the original input features through utilizing different activation functions, but also provides information diversity and increases the number of input nodes for classifier by parallel-connected strategy. Experimental results show that the BAF yields better-learned features and classification performances.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84794305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.4018/IJCINI.20211001.OA43
Yanping Yang, Ruiguang Li
For the system with unknown statistical property noises, the property that the energies of the system noise and the observation noise are limited is utilized in this paper. On this basis, two novel fusion algorithms are proposed for ship-integrated navigation with the relative navigation information broadcasted by the automatic identification systems (AISs) in the adjacent ships. Firstly, an H¥ fusion filtering algorithm is given to deal with the navigation observation messages under the centralized fusion framework. The integrated navigation method based on this algorithm cannot deal with the asynchronous navigation messages in real time. Therefore, a sequential H¥ fusion-filtering algorithm is also given to sequentially deal with the asynchronous navigation messages. Finally, a computer simulation is employed to illustrate the validity and feasibility of the sequential method.
{"title":"Relatively-Integrated Ship Navigation by H¥ Fusion Filters","authors":"Yanping Yang, Ruiguang Li","doi":"10.4018/IJCINI.20211001.OA43","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA43","url":null,"abstract":"For the system with unknown statistical property noises, the property that the energies of the system noise and the observation noise are limited is utilized in this paper. On this basis, two novel fusion algorithms are proposed for ship-integrated navigation with the relative navigation information broadcasted by the automatic identification systems (AISs) in the adjacent ships. Firstly, an H¥ fusion filtering algorithm is given to deal with the navigation observation messages under the centralized fusion framework. The integrated navigation method based on this algorithm cannot deal with the asynchronous navigation messages in real time. Therefore, a sequential H¥ fusion-filtering algorithm is also given to sequentially deal with the asynchronous navigation messages. Finally, a computer simulation is employed to illustrate the validity and feasibility of the sequential method.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86799821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.4018/IJCINI.20211001.OA38
T. Gururaj, G. Siddesh
{"title":"Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching","authors":"T. Gururaj, G. Siddesh","doi":"10.4018/IJCINI.20211001.OA38","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA38","url":null,"abstract":"","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74031161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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